PredictED– Dublin City University (LAEP Inventory)

Dublin City University (DCU) initiated a new learning analytics programme called PredictED in 2014 for ten modules.

PredictED analyses student behaviours in the Moodle virtual learning environment (VLE), and compares them with previously successful students on the same module.

Once a week, participating students receive an email with an updated prediction of whether they are likely to pass or fail the module. Those who appear to be struggling receive study suggestions and resources to support their study. The emails also contain information about how their VLE activity compared with that of their classmates during the previous week.

Classification

Inventory type:

pilot

Keywords:

predictive analytics, self-regulation

Context of Practice

Learning:

post-compulsory

Geographical:

national: Ireland

Pedagogic:

The approach taken by PredictED has not been explicit in respect of pedagogy. The system focuses on student support through the use of predictive analytics. Use of the system is by students for self-regulation, and it does not appear to involve staff to intercede with interventions.

Practical Matters

Tools used:

PredictED was developed by DCU’s Insight Centre for Data Analytics. It functions within the university’s VLE system, Moodle, although no contractual relationship between PredictED and Moodle is explicitly stated.

Design and implementation:

The programme is currently only available for a small number of modules. Students must opt in to participate. The system is designed for first-year students in their first term at the university. During the initial trial, around 75% of eligible students opted to participate.

Maturity and Evidence of Utility

Informal accounts highlight that students who opted to participate in the PredictED trial had 3% higher scores than those who did not participate. However, this perceived improvement does not take into account self-selection bias or consider demographics of those who opted in versus those who did not. Thus, more rigorous testing of the system is needed to further determine the system’s maturity and evidence of utility.